class_docstr: Data class that bundles associated information to use pretrained
  :py:class:`~torchaudio.models.SquimObjective` model.
  
  This class provides interfaces for instantiating the pretrained model along with
  the information necessary to retrieve pretrained weights and additional data
  to be used with the model.
  
  Torchaudio library instantiates objects of this class, each of which represents
  a different pretrained model. Client code should access pretrained models via these
  instances.
  
  This bundle can estimate objective metric scores for speech enhancement, such as STOI, PESQ, Si-SDR.
  A typical use case would be a flow like `waveform -> list of scores`. Please see below for the code example.
  
  Example: Estimate the objective metric scores for the input waveform.
      >>> import torch
      >>> import torchaudio
      >>> from torchaudio.pipelines import SQUIM_OBJECTIVE as bundle
      >>>
      >>> # Load the SquimObjective bundle
      >>> model = bundle.get_model()
      Downloading: "https://download.pytorch.org/torchaudio/models/squim_objective_dns2020.pth"
      100%|████████████| 28.2M/28.2M [00:03<00:00, 9.24MB/s]
      >>>
      >>> # Resample audio to the expected sampling rate
      >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
      >>>
      >>> # Estimate objective metric scores
      >>> scores = model(waveform)
      >>> print(f"STOI: {scores[0].item()},  PESQ: {scores[1].item()}, SI-SDR: {scores[2].item()}.")
docstr: Construct the SquimObjective model, and load the pretrained weight.
  
  The weight file is downloaded from the internet and cached with
  :func:`torch.hub.load_state_dict_from_url`
  
  Args:
      dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`.
  
  Returns:
      Variation of :py:class:`~torchaudio.models.SquimObjective`.
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code: def get_model(self, *, dl_kwargs=None) -> SquimObjective:
      """Construct the SquimObjective model, and load the pretrained weight.
  
      The weight file is downloaded from the internet and cached with
      :func:`torch.hub.load_state_dict_from_url`
  
      Args:
          dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`.
  
      Returns:
          Variation of :py:class:`~torchaudio.models.SquimObjective`.
      """
      model = squim_objective_base()
      model.load_state_dict(self._get_state_dict(dl_kwargs))
      model.eval()
      return model
  
file_path: audio/src/torchaudio/pipelines/_squim_pipeline.py
imports: from dataclasses import dataclass
  from torchaudio._internal import load_state_dict_from_url
  from torchaudio.models import squim_objective_base, squim_subjective_base, SquimObjective, SquimSubjective
label_desc: Use image node_id 2 for calling the SquimObjectiveBundle obj's underlying member method code with example usage: obj.get_model() and returns: model